One Weekend: How I Found 3,000 Open-Source AI Projects (and You Can Too)

I gave myself 48 hours to map the open-source AI landscape. Not to use any of it — just to understand what exists. I came out with a list of ~3,000 projects, categorized. The method is repeatable, and it doesn't require knowing where to start.

Here's the exact process.

Step 1: Awesome lists as the index

The awesome-* GitHub repos are the closest thing to a curated index. Start with:

• awesome-llm — the big generalist list
• awesome-machine-learning — broadest coverage
• awesome-generative-ai — the newer, faster-moving space
• awesome-local-llm — if you care about running things on your own hardware

These aren't just lists — each entry is a link with a one-line description. I scraped them into a single file and deduplicated. That alone gave me ~1,200 unique projects.

Step 2: GitHub topic tags

GitHub's topic system is underrated for discovery. Searching by topic surfaces projects that maintainers themselves tagged. High-signal topics:

• llm, large-language-model
• rag, retrieval-augmented-generation
• local-llm, llama-cpp, gguf
• ai-agents, agent-framework
• mlx, onnx, gguf — for the runtime/inference layer

Searching topic:llm stars:>100 gives you the projects people actually use. Dropping the star threshold and sorting by recently-updated gives you the long tail.

Step 3: Follow the dependency graph

This is where it gets powerful. Pick a tool you already trust and look at what it depends on, or what depends on it. If you use one inference runtime, the projects wrapping it, fine-tuning for it, or benchmarking it are all one hop away. Two hops and you've tripled your map.

Step 4: Paper-to-code

ArXiv papers often link a reference implementation. Searching GitHub for the paper title or the repo mentioned in the abstract finds code that hasn't been "discovered" yet — sometimes months before it hits an awesome list.

What I learned

The 3,000 projects collapse into maybe 30 real categories, and maybe 200 that are actively maintained. The signal-to-noise is brutal, but the discovery method is fast once you know it.

The biggest surprise: the local-inference ecosystem (llama.cpp and everything built on it) is far more active than the API-wrapper projects. People are building tools that run on the model, not around someone's API.

Has anyone else mapped this space? I'd love to compare categorization — I suspect I'm missing whole branches.